用多智能体系统整合视频代码文档等资源，提升在线学习效率
Enhancing Online Learning Efficiency Through Heterogeneous Resource Integration with a Multi-Agent RAG System
- 设计多智能体RAG系统，分类型自动检索和融合异构学习资源
- 初步用户研究显示系统易用性强，知识获取效率显著提升
- 适合需要高效整合多方资料的学习者与教育技术开发者
高效在线学习依赖对视频、代码仓库、文档及通用网页内容等多种资源的无缝访问。本文介绍早期阶段的多智能体检索增强生成（Multi-Agent RAG）系统，旨在通过整合这些异构资源来提升学习效率。系统采用针对特定资源类型（如YouTube教程、GitHub仓库、文档网站、搜索引擎）定制的专用智能体，实现相关信息的自动化检索与综合。该方法简化了知识查找与整合流程，降低人工成本，改善学习体验。初步用户研究表明，系统具有强可用性与中高实用性，展现了在提升知识获取效率方面的潜力。
原文摘要 · Abstract (English)
Efficient online learning requires seamless access to diverse resources such as videos, code repositories, documentation, and general web content. This poster paper introduces early-stage work on a Multi-Agent Retrieval-Augmented Generation (RAG) System designed to enhance learning efficiency by integrating these heterogeneous resources. Using specialized agents tailored for specific resource types (e.g., YouTube tutorials, GitHub repositories, documentation websites, and search engines), the system automates the retrieval and synthesis of relevant information. By streamlining the process of finding and combining knowledge, this approach reduces manual effort and enhances the learning experience. A preliminary user study confirmed the system's strong usability and moderate-high utility, demonstrating its potential to improve the efficiency of knowledge acquisition.
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